Predicting click-through rates is a massive-scale learning problem that is central to the multi-billion dollar online advertisement industry. The online advertising is become significant element for ad revenue generation. Choosing the right ads for the query and the order in which they are displayed greatly affects the probability that the user will see and click the advertisement. This ranking has a strong impact on the revenue the online advertising receives from the ads. For this reason, it is important to accurately estimate the click through rates of ads.
Traditionally, predictive analytics required a tedious process. A typical flow of activities involved in predictive analytics is identifying and extracting the data required from the sources such as an Enterprise Resource Planning (ERP) or a Customer Relationship Management (CRM) system or coming from the data warehouse. Different predictive analytic tools have different requirements on how to best process the data. For example, the data requires some transformation to fit the requirements of the predictive analytic tools so that analysis can take place successfully After the analysis, the noise in the data is removed and conclusions are drawn that lead to changes in for instance customer segmentation or product clustering. Further, success of analysis is monitored and the cycle starts all over.
The key assumption of the traditional predictive analytics process is that it is best to take data to the tools. Thus, this approach is costly, takes too much time, robs the process of its creativity and doesn't allow the number of experiments to scale.
In light of the above discussion, there is a need for a method and system, which overcomes all the above stated problems.